What Is InfiniBand?
InfiniBand is a networking interconnect built for high bandwidth and very low latency. In AI infrastructure it is the fabric that links GPUs to each other and nodes to nodes inside a cluster. Its defining feature is RDMA, remote direct memory access, which lets one machine read and write another machine’s memory directly, without routing every transfer through the CPU and operating system.
That direct path is why InfiniBand matters for AI. Distributed training and multi-node inference generate enormous amounts of GPU-to-GPU communication, and on a conventional network that traffic becomes the bottleneck. InfiniBand moves it with the bandwidth and latency that keep the GPUs fed.
Why It Matters for AI Workloads
When a training job is spread across dozens or hundreds of GPUs, the GPUs must constantly exchange gradients and parameters. If the network cannot keep up, expensive accelerators sit idle waiting for data, and the effective throughput of the whole cluster drops. High-bandwidth InfiniBand (current host links reach into the multiple-terabit-per-second range) reduces that communication bottleneck so scaling to more GPUs actually delivers more useful compute.
The same property helps inference at scale, where token traffic and model sharding place their own demands on the fabric. This is why purpose-built GPU clouds and AI factories specify InfiniBand or comparably tuned Ethernet as a core part of the design rather than an afterthought.
InfiniBand and Saturn Cloud
InfiniBand is a property of the underlying infrastructure. What teams need is a way to use clusters wired this way without managing the networking themselves.
Saturn Cloud runs on GPU infrastructure from neoclouds and other providers that offer InfiniBand fabrics, and handles the orchestration on top: placing distributed training and inference on interconnected nodes and setting the environment so multi-node jobs use the fast network. Teams get the interconnect’s benefit through the platform rather than by wiring it up themselves.
